Feasibility and Efficacy of the Alert Program® for Children with Attention-Deficit/Hyperactivity Disorder
Bibliographic record
Abstract
This study explored the feasibility and initial efficacy of a sensorimotor intervention to improve symptoms of attention-deficit/hyperactivity disorder (ADHD). Twenty-seven children (ages 8-12 years) with ADHD and their parents participated in an 8-week group intervention based on The Alert Program® for Self-Regulation (AP). Families were taught to recognize child arousal states and to use sensorimotor strategies to manage levels of alertness. Parent and teacher reports of child attention symptoms were collected at baseline, before and after intervention. Objective ratings of child problem behaviours and use of sensorimotor strategies during computerized tasks of visual and auditory attention were also coded before and after intervention. Parents and children endorsed high acceptability and satisfaction for the AP treatment. Parental ratings indicated increased knowledge and use of sensorimotor strategies, and decreased child ADHD symptoms at home from pre-AP to post-AP. However, no significant changes in child outcomes were reported by teachers. Unexpectedly, observed child problem behaviours during the visual attention task increased from pre-AP to post-AP. The AP was received positively by parents and children with improvements in regulation strategies and child attention at home, but more work needs to be done to generalize the effects to school and other peer settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".